- Professor, Statistics
- Engineering 2, Room 539B
- By appointment
- David Draper is a Professor of Statistics in the Department of Applied Mathematics and Statistics (in the Baskin School of Engineering) at the University of California, Santa Cruz (UCSC). He received his Ph.D. in 1981 from the University of California, Berkeley, and has since taught and worked on research projects at the University of Chicago; the RAND Corporation; the University of Washington; the University of California, Los Angeles; the University of Bath (U.K.); the University of Neuchatel (Switzerland); and UCSC. He is a fellow of the American Association for the Advancement of Science, the American Statistical Association, the Institute of Mathematical Statistics and the Royal Statistical Society. His research concerns methodological developments in Bayesian statistics, with particular emphasis on hierarchical modeling, Bayesian nonparametric methods, model specification and model uncertainty, quality assessment, risk assessment, and applications in the environmental, medical, and social sciences; he is the author or co-author of 92 contributions to the research literature (4 books, 45 journal articles, 2 letters, 6 book chapters, 2 book reviews, 3 encyclopedia articles, 28 invited discussions and 2 contributed discussions). Since 1993 he has been PI or co-PI on 18 grants totaling about $5.0 million. From 2001 to 2007 he served as the founding chair of the Applied Mathematics and Statistics Department at UCSC, and in 2002 he was President of the International Society for Bayesian Analysis. From 1994 to the present he has given 34 short courses on Bayesian methods and applications in Brazil, Canada, Finland, Greece, India, New Zealand, Switzerland, the U.K. and the U.S.. Two of these short courses won Excellence in Continuing Education awards from the American Statistical Association; he has also won campus-wide teaching awards at the University of Chicago and UCSC. Since 1993 he has given 63 invited, special invited or plenary talks at major research conferences and leading statistics departments in Australia, Austria, Brazil, Canada, Chile, China, the Czech Republic, Denmark, Germany, Greece, India, Israel, Italy, the Netherlands, Portugal, Singapore, Spain, Switzerland, Taiwan, the U.K. and the U.S. He has been an Associate Editor for 6 leading journals, and he has organized or co-organized 6 international research conferences. Since 1991 he has mentored 17 graduate students to M.S. and/or Ph.D. degrees at universities in Sweden, the U.K. and the U.S., and he has supervised 3 post-doctoral research associates at Bath and UCSC. From 1980 to the present he has taught more than 7,000 undergraduates and 650 graduate students in 73 classes and 28 individual graduate student supervisions at 8 universities in Switzerland, the U.K. and the U.S. He has a particular interest (a) in developing new statistical methodologies in the context of solving important real problems and (b) in effectively communicating complex statistical and scientific ideas to diverse audiences.
- Bayesian statistics, hierarchical modeling, Bayesian nonparametric methods, model specification and model uncertainty, quality assessment, risk assessment, statistical applications in the environmental, medical, and social sciences
- Draper D (1995). Assessment and propagation of model uncertainty (with discussion). Journal of the Royal Statistical Society (Series B), 57, 45-97.
- Escobar GJ, Greene JD, Scheirer P, Gardner MN, Draper D, Kipnis P (2008). Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient and laboratory databases (with discussion). Medical Care, 46, 232-239.
- Fouskakis D, Draper D (2008). Comparing stochastic optimization methods for variable selection in binary outcome prediction, with application to health policy. Journal of the American Statistical Association, 103, 1367-1381.
- Fouskakis D, Ntzoufras I, Draper D (2009a). Bayesian variable selection using cost-adjusted BIC, with application to cost-effective measurement of quality of health care. Annals of Applied Statistics, 3, 663-690.
- Fouskakis D, Ntzoufras I, Draper D (2009b). Population-based reversible-jump MCMC for Bayesian variable selection and evaluation under cost constraints. Journal of the Royal Statistical Society (Series C), 58, 383-403.
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